Concentrated suspensions of Brownian beads in water: dynamic heterogeneities trough a simple experimental technique

Concentrated suspensions of Brownian hard-spheres in water are an epitome for understanding the glassy dynamics of both soft materials and supercooled molecular liquids. From an experimental point of view, such systems are especially suited to perform particle tracking easily, and, therefore, are a benchmark for novel optical techniques, applicable when primary particles cannot be resolved. Differential Variance Analysis (DVA) is one such novel technique that simplifies significantly the characterization of structural relaxation processes of soft glassy materials, since it is directly applicable to digital image sequences of the sample. DVA succeeds in monitoring not only the average dynamics, but also its spatio-temporal fluctuations, known as dynamic heterogeneities. In this work, we study the dynamics of dense suspensions of Brownian beads in water, imaged through digital video-microscopy, by using both DVA and single-particle tracking. We focus on two commonly used signatures of dynamic heterogeneities: the dynamic susceptibility, $\chi_4$, and the non-Gaussian parameter, $\alpha_2$. By direct comparison of these two quantities, we are able to highlight similarities and differences. We do confirm that $\chi_4$ and $\alpha_2$ provide qualitatively similar information, but we find quantitative discrepancies in the scalings of characteristic time and length scale on approaching the glass transition.Comment: The original publication is available at http://www.scichina.com and http://www.springerlink.com http://engine.scichina.com/publisher/scp/journal/SCPMA/doi/10.1007/s11433-019-9401-x?slug=abstrac

The measurement of boundary layers on a compressor blade in cascade. Volume 1: Experimental technique, analysis and results

Measurements were made of the boundary layers and wakes about a highly loaded, double-circular-arc compressor blade in cascade. These laser Doppler velocimetry measurements have yielded a very detailed and precise data base with which to test the application of viscous computational codes to turbomachinery. In order to test the computational codes at off-design conditions, the data were acquired at a chord Reynolds number of 500,000 and at three incidence angles. Moreover, these measurements have supplied some physical insight into these very complex flows. Although some natural transition is evident, laminar boundary layers usually detach and subsequently reattach as either fully or intermittently turbulent boundary layers. These transitional separation bubbles play an important role in the development of most of the boundary layers and wakes measured in this cascade and the modeling or computing of these bubbles should prove to be the key aspect in computing the entire cascade flow field. In addition, the nonequilibrium turbulent boundary layers on these highly loaded blades always have some region of separation near the trailing edge of the suction surface. These separated flows, as well as the subsequent near wakes, show no similarity and should prove to be a challenging test for the viscous computational codes

Qualitative grading of aortic regurgitation: a pilot study comparing CMR 4D flow and echocardiography.

Over the past 10 years there has been intense research in the development of volumetric visualization of intracardiac flow by cardiac magnetic resonance (CMR).This volumetric time resolved technique called CMR 4D flow imaging has several advantages over standard CMR. It offers anatomical, functional and flow information in a single free-breathing, ten-minute acquisition. However, the data obtained is large and its processing requires dedicated software. We evaluated a cloud-based application package that combines volumetric data correction and visualization of CMR 4D flow data, and assessed its accuracy for the detection and grading of aortic valve regurgitation using transthoracic echocardiography as reference. Between June 2014 and January 2015, patients planned for clinical CMR were consecutively approached to undergo the supplementary CMR 4D flow acquisition. Fifty four patients(median age 39 years, 32 males) were included. Detection and grading of the aortic valve regurgitation using CMR4D flow imaging were evaluated against transthoracic echocardiography. The agreement between 4D flow CMR and transthoracic echocardiography for grading of aortic valve regurgitation was good (j = 0.73). To identify relevant,more than mild aortic valve regurgitation, CMR 4D flow imaging had a sensitivity of 100 % and specificity of 98 %. Aortic regurgitation can be well visualized, in a similar manner as transthoracic echocardiography, when using CMR 4D flow imaging

Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7 figure

Process monitoring and visualization solutions for hot-melt extrusion : a review

Objectives: Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Key Findings: Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. Summary: This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level

Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods